Abstract
With the rapid development of cities, the geographic information of urban blocks is also changing rapidly. However, traditional methods of updating road data cannot keep up with this development because they require a high level of professional expertise for operation and are very time-consuming. In this paper, we develop a novel method for extracting missing roadways by reconstructing the topology of the roads from big mobile navigation trajectory data. The three main steps include filtering of original navigation trajectory data, extracting the road centerline from navigation points, and establishing the topology of existing roads. First, data from pedestrians and drivers on existing roads were deleted from the raw data. Second, the centerlines of city block roads were extracted using the RSC (ring-stepping clustering) method proposed herein. Finally, the topologies of missing roads and the connections between missing and existing roads were built. A complex urban block with an area of 5.76 square kilometers was selected as the case study area. The validity of the proposed method was verified using a dataset consisting of five days of mobile navigation trajectory data. The experimental results showed that the average absolute error of the length of the generated centerlines was 1.84 m. Comparative analysis with other existing road extraction methods showed that the F-score performance of the proposed method was much better than previous methods.
Highlights
With rapid road construction development in urban and rural areas, the consequent road changes result in lagging road-data updates that are a poor match to the current situation and have low integrity and accuracy
Selecting GNSS trajectory points not located on existing roads is usually a necessary stage in road mapping in order to update road networks and refine the geometry of road segments or intersections [5,15,16,17,18,19]
Reference [31] modeled the speed of the tracks from the centerline of the road as a Gaussian distribution, and extracted the directionality and turning restriction attributes for road maps such as open street map (OSM)
Summary
With rapid road construction development in urban and rural areas, the consequent road changes result in lagging road-data updates that are a poor match to the current situation and have low integrity and accuracy. GNSS (global navigation satellite system) trajectories, and multisource data fusion are some of the major data sources used for the renewal and repair of missing GIS (geographical information system) road network data [3] With technological developments such as wireless communications, Big Data, and cloud computing, navigation trajectories are gradually becoming the main data source for urban road updates. The rest of the paper is organized as follows: Section 3 describes the research methodology in detail; Section 4 presents a case study with the adopted data and evaluates the quality of the proposed method; and Section 5 derives and discusses the main conclusions and concerns related to the proposed method
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